Complex engineering systems such as nuclear power plants are often subjected to off-normal situations that arise from component malfunctions, operational transients or external events. As system complexity increases, the demands on plant operators for fast, accurate responses to these events also increase, to the point where a computer-based advisory system to supplement operator training is highly desirable.
Operation of these systems, especially those with the potential for severe consequences in the event of an off-normal occurrence, requires the assimilation and processing of large amounts of data from system monitoring equipment. Installations such as power plants, chemical processing plants and fuel fabrication plants can all benefit from the application of computer-based expert systems to provide input to the operators as an aid in the diagnosis of plant faults and transient recovery. Such a system could extend the expertise of the operators to situations beyond their training envelope and provide rapid assistance during low-probability events requiring an extended time for the operators to diagnose and develop a response.
Research and development efforts in the area of operator-assistance systems for the diagnosis and management of plant transients, especially in nuclear power plants, have been in progress for many years, with various approaches and varying degrees of success. Typical systems are based on signal pattern recognition and simulator engines, or expert systems, that incorporate automated reasoning and neural network algorithms. It is highly probable that as the availability of low-cost, high-performance computers increases, operator advisory systems will be a standard feature of future generation plants. The systems will be used to aid in the diagnosis of component failures or off-normal events, as well as in the management of the plant transients that often follow such events.
Prior expert system approaches are limited to the use of predetermined sets of malfunction and associated operator actions which do not account for unanticipated malfunctions. Examples of this type of process fault diagnosis in control systems can be found in U.S. Pat. Nos. 5,265,035 and 5,442,555, assigned to the assignee of the present application. The disclosures of these two patents are hereby incorporated by reference in the present application. The present invention takes into account both anticipated and unanticipated system component malfunctions to provide realignment procedures and operator actions at on-line speeds to allow the system to be either safely shut down or to continue operation at full or partial capacity.